{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnt = arcpy.Point(116.403927,39.915087)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sr = arcpy.SpatialReference(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptg = arcpy.PointGeometry(inputs=pnt,spatial_reference=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"area: \",ptg.area)\n",
    "print(\"length: \",ptg.length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptg.extent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### angleAndDistanceTo示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1_sd = arcpy.PointGeometry(inputs=arcpy.Point(116.5930544,40.0795454),\n",
    " spatial_reference=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt2_dx = arcpy.PointGeometry(inputs=arcpy.Point(116.4168081, 39.5129736),\n",
    " spatial_reference=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1_sd.angleAndDistanceTo(pt2_dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(pt1_sd.angleAndDistanceTo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 北京各区中心与首都中心的距离分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 定义天安门的点要素，并且设定空间参考为4326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt_tam = arcpy.PointGeometry(inputs=arcpy.Point(116.403927,39.915087),\n",
    "spatial_reference=sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 利用da包里面查询游标，把北京行政区里面的各区几何信息和名称取出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnt , name = [],[]\n",
    "with arcpy.da.SearchCursor(\"../data/beijing/bj_area.shp\",\n",
    "[\"SHAPE@\",\"Name\"]) as cur:\n",
    "    for row in cur:\n",
    "        pnt.append(row[0])\n",
    "        name.append(row[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 先求出每个区域的几何中心点位置。\n",
    "* 然后逐个计算与天安门的距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = []\n",
    "for p in pnt:\n",
    "    dist.append(pt_tam.angleAndDistanceTo(p.centroid)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 把计算结果可视化算出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(x=list(range(len(dist))),height=dist)\n",
    "plt.xticks(list(range(len(dist))),name,rotation=90)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.11 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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